• The Journal of the Korea Contents Association
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• v.11 no.11
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• pp.42-48
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• 2011

This paper presents a bridge road surface frost prediction and monitoring system. The node sensing hardware comprises microprocessor, temperature sensors, humidity sensors and Zigbee wireless communication. A software interface is implemented the control center to monitor and acquire the temperature and humidity data of bridge road surface. A bridge road surface frost occurs when the bridge deck temperature drops below the dew point and the freezing point. Measurement data was used for prediction of road surface frost occurrences. The actual alert is performed at least 30 minutes in advance the road surface frost. The road surface frost occurrences data are sent to nearby drivers for traffic accidents prevention purposes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2010.05a
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• pp.1077-1078
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• 2010

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.6
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• pp.101-107
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• 2020

Road surface measurement is an essential process for quantifying the degree and displacement of roughness in road surface management. For safer road surface management and quick maintenance, it is important to accurately measure the road surface while mounted on a vehicle. In this paper, we propose a sophisticated road surface measurement system that can be measured on a moving vehicle. The proposed road surface measurement system supports more accurate measurement of the road surface by using a high-performance line laser sensor. It is also possible to measure the transverse and longitudinal profile by matching the position information acquired from the RTK, and the velocity adaptive update algorithm allows a manager to monitor in a real-time manner. In order to evaluate the proposed system, the Gocator laser sensor, MRP module, and NVIDIA Xavier processor were mounted on a test mobile and tested on the road surface. Our evaluation results demonstrate that our system measures accurate profile base on the MSE. Our proposed system can be used not only for evaluating the condition of roads but also for evaluating the impact of adjacent excavation.

• Structural Monitoring and Maintenance
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• v.2 no.1
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• pp.65-75
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• 2015

Among the destruction instances of half-through arch bridges, the shorter hangers are more likely to be ruined. For a thorough investigation of the hanger system durability, we have studied vehicle impact effect on hangers with vehicle-bridge coupling method for a half-through concrete-filled-steel-tube arch bridge. A numerical method has been applied to simulate the variation of dynamic internal force (stress) in hangers under different vehicle speeds and road surface roughness. The characteristics and differences in impact effect among hangers with different length (position) are compared. The impact effect is further analyzed comprehensively based on the vehicle speed distribution model. Our results show that the dynamic internal force induced by moving vehicles inside the shorter hangers is significantly greater than that inside the longer ones. The largest difference of dynamic internal force among the hangers could be as high as 28%. Our results well explained a common phenomenon in several hanger damage accidents occurred in China. This work forms a basis for hanger system's fatigue analysis and service life evaluation. It also provides a reference to the design, management, maintenance, monitoring, and evaluation for this kind of bridge.

• Smart Structures and Systems
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• v.24 no.6
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• pp.745-757
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• 2019

Road surface deteriorations such as potholes have caused motorists heavy monetary damages every year. However, effective road condition monitoring has been a continuing challenge to road owners. Depth cameras have a small field of view and can be easily affected by vehicle bouncing. Traditional image processing methods based on algorithms such as segmentation cannot adapt to varying environmental and camera scenarios. In recent years, novel object detection methods based on deep learning algorithms have produced good results in detecting typical objects, such as faces, vehicles, structures and more, even in scenarios with changing object distances, camera angles, lighting conditions, etc. Therefore, in this study, a Deep Learning Pothole Detector (DLPD) based on the deep region-based convolutional neural network is proposed for autonomous detection of potholes from images. About 900 images with potholes and road surface conditions are collected and divided into training and testing data. Parameters of the network in the DLPD are calibrated based on sensitivity tests. Then, the calibrated DLPD is trained by the training data and applied to the 215 testing images to evaluate its performance. It is demonstrated that potholes can be automatically detected with high average precision over 93%. Potholes can be differentiated from manholes by training and applying a manhole-pothole classifier which is constructed using the convolutional neural network layers in DLPD. Repeated detection of the same potholes can be prevented through feature matching of the newly detected pothole with previously detected potholes within a small region.

• The Journal of Korean Institute of Information Technology
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• v.16 no.12
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• pp.57-68
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• 2018

A system to prevent disasters by collecting and analyzing environmental information such as road surface sedimentation, sinkholes, collapse risk of bridges, temperature and humidity around tram station is continuously monitored by monitoring the condition of road surface when constructing tram which is one of the urban railways. In this paper, we propose a wireless network security management system for tram roads based on LPWA that can recognize risk factors of road surface, bridge and tram station of tram in advance and prevent risk. The proposed system consists of a sensor node that detects the state of the tram road surface, a gateway that collects sensor information, and a safety management system that monitors the safety and environmental conditions of the tram road surface, and applies the low power long distance communication technology. As a result of comparing the proposed system with the LTE system in the field test, it was confirmed that there is no significant difference between the sensor information value and the critical alarm level in the monitoring system.

• Smart Structures and Systems
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• v.32 no.5
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• pp.309-318
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• 2023

The appropriate maintenance of highway roads is critical for the safe operation of road networks and conserves maintenance costs. Multiple methods have been developed to investigate the surface of roads for various types of cracks and potholes, among other damage. Like road surface damage, the condition of expansion joints in concrete pavement is important to avoid unexpected hazardous situations. Thus, in this study, a new system is proposed for autonomous expansion joint monitoring using a vision-based system. The system consists of the following three key parts: (1) a camera-mounted vehicle, (2) indication marks on the expansion joints, and (3) a deep learning-based automatic evaluation algorithm. With paired marks indicating the expansion joints in a concrete pavement, they can be automatically detected. An inspection vehicle is equipped with an action camera that acquires images of the expansion joints in the road. You Only Look Once (YOLO) automatically detects the expansion joints with indication marks, which has a performance accuracy of 95%. The width of the detected expansion joint is calculated using an image processing algorithm. Based on the calculated width, the expansion joint is classified into the following two types: normal and dangerous. The obtained results demonstrate that the proposed system is very efficient in terms of speed and accuracy.

• Journal of Environmental Science International
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• v.19 no.6
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• pp.665-670
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• 2010

Pollutants from urban pavement consists various kinds of substances which are originated from dry deposition, a grind out tire, corrosive action of rain to pavement and facilities and raw materials of the road etc.. These are major pollutants of urban NPS (Non-point source) during rainfall period. However there is not enough information to control such pollutants for appropriate management of natural water quality. In this study of transportation areas, three monitoring stations were set up at trunk road, urban highway and national road in Gyeongnam province. Runoff flow rate was measured at every 15minutes by automatic flow meters installed at the end of storm sewer pipe within the road catchment area for water quality analysis. Data was collected every 15 minutes for initial two hours of rainfall. Additional samples were collected 1-4 hours interval till the end of rainfall. The monitoring parameters were $COD_{Mn}$, SS, T-N & T-P and heavy metals. The average EMCs of TSS and $COD_{Mn}$ were 62.0 mg/L and 24.2 mg/L on the city trunk road, which were higher than those of urban highway and national road, indicating higher pollutant loads due to activities in the city downtown area beside the vehicle. On the other hand, the average EMC of T-N and T-P were in the range of 2.67-3.23 mg/L and 0.19-3.21 mg/L for all the sampling sites. Heavy metals from the roads were mainly Fe, Zn, Cu and Mn, showing variable EMCs by the type of road. From the TSS wash-off analysis in terms of FF(first flush) index, first flush phenomenon was clearly observed in the trunk road(FF : 0.89-1.43). However, such mass delivery behavior was not apparently shown in urban highway(FF : 0.90-1.11) and national road(FF : 0.81-1.41).

• Smart Media Journal
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• v.13 no.3
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• pp.18-26
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• 2024

Road surface monitoring is essential for maintaining road environment safety through managing risk factors like rutting and crack detection. Using autonomous driving-based UGVs with high-performance 2D laser sensors enables more precise measurements. However, the increased energy consumption of these sensors is limited by constrained battery capacity. In this paper, we propose a fusion sensor system for efficient surface monitoring with UGVs. The proposed system combines color information from cameras and depth information from line laser sensors to accurately detect surface displacement. Furthermore, a dynamic sampling algorithm is applied to control the scanning frequency of line laser sensors based on the detection status of monitoring targets using camera sensors, reducing unnecessary energy consumption. A power consumption model of the fusion sensor system analyzes its energy efficiency considering various crack distributions and sensor characteristics in different mission environments. Performance analysis demonstrates that setting the power consumption of the line laser sensor to twice that of the saving state when in the active state increases power consumption efficiency by 13.3% compared to fixed sampling under the condition of λ=10, µ=10.

• Journal of Korean Society of Transportation
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• v.33 no.3
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• pp.284-293
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• 2015

The objective of this paper is to determine the conditions of road surface by utilizing the images collected from closed-circuit television (CCTV) cameras installed on roadside. First, a technique was examined to detect wet surfaces at nighttime. From the literature reviews, it was revealed that image processing using polarization is one of the preferred options. However, it is hard to use the polarization characteristics of road surface images at nighttime because of irregular or no light situations. In this study, we proposes a new discriminant for detecting wet and dry road surfaces using CCTV image data at night. To detect the road surface conditions with night vision, we applied the wavelet packet transform for analyzing road surface textures. Additionally, to apply the luminance feature of night CCTV images, we set the intensity histogram based on HSI(Hue Saturation Intensity) color model. With a set of 200 images taken from the field, we constructed a detection criteria hyperplane with SVM (Support Vector Machine). We conducted field tests to verify the detection ability of the wet road surfaces and obtained reliable results. The outcome of this study is also expected to be used for monitoring road surfaces to improve safety.